Journal of Threatened Taxa |
www.threatenedtaxa.org | 26 June 2024 | 16(6): 25318–25329
ISSN 0974-7907
(Online) | ISSN 0974-7893 (Print)
https://doi.org/10.11609/jott.8758.16.6.25318-25329
#8758 | Received 30
September 2023 | Final received 15 February 2024 | Finally accepted 10 April
2024
Understanding Human-Nilgai
negative interactions in India: a systematic review through print media report
analysis
Chandrapratap Singh Chandel
1# ,
Sangeeta Madan 2# , Dhruv Jain 3 , Lallianpuii
Kawlni 4 ,
Vishnupriya Kolipakam
5 & Qamar Qureshi 6
1,2 Department of Zoology &
Environmental Science, Gurukula Kangri (Deemed to be)
University, Haridwar, Uttarakhand 249404, India.
1,3.4.5,6 Wildlife Institute of India, Chandrabani, Dehradun, Uttarakhand 248001, India.
1 chandrabiology35@gmail.com (corresponding
author), 2 snmadan21@gmail.com, 3 dhruv97@wii.gov.in, 4
lallian@wii.gov.in, 5 vishnupriya@wii.gov.in, 6 qnq@wii.gov.in
# These authors have equal
contribution
Editor: David Mallon, Manchester Metropolitan
University, Manchester, UK. Date
of publication: 26 June 2024 (online & print)
Citation: Chandel, C.S., S. Madan, D. Jain, L. Kawlni, V. Kolipakam & Q.
Qureshi (2024). Understanding Human-Nilgai negative interactions in India: a
systematic review through print media report analysis. Journal of Threatened Taxa 16(6):
25318–25329. https://doi.org/10.11609/jott.8758.16.6.25318-25329
Copyright: © Chandel et al. 2024. Creative Commons Attribution 4.0
International License. JoTT allows unrestricted use,
reproduction, and distribution of this article in any medium by providing
adequate credit to the author(s) and the source of publication.
Funding: This study did not obtain any funding or other financial support from any governmental or non-governmental organizations.
Competing interests: The authors declare no competing interests.
Author details: Sangeeta Madan (SM) is an associate professor with more than 13 years of research experience in water pollution, vermicomposting, and air pollution. Chandrapratap Singh Chandel (CSC) is a PhD scholar under SM and affiliated with Wildlife Institute of India, working on the human-nilgai negative interactions. Dhruv Jain (DJ) is a project fellow with a specialisation in GIS application. Lallianpuii Kawlni (LK)
is a faculty member (scientist-D). Her expertise is in wildlife health monitoring. Vishnupriya Kolipakam (VK)
is a faculty member (scientist-D). She is a population geneticist with a specialisation in evolutionary biology and phylogenetics. Qamar Qureshi (QQ)
is a senior scientist and professor. He specialises in GIS and RS applications with more than 30 years of experience in wildlife research.
Author contributions: Concept, design, & supervision: LK, VK & QQ; Data collection & analysis: CSC, SM, DJ; Manuscript writing CSC; Manuscript review & comments: SM.
Acknowledgements: We are grateful to the University Grants Commission, New Delhi, India, for providing a junior research fellow grant to CSC while working on this article. The study obtained no additional financial support from
public, commercial, or nonprofit entities.
Abstract: Despite being one of the most
conflict-prone species in India, the Nilgai antelope Boselaphus
tragocamelus has received little scientific
attention. In this study, we address this knowledge gap by conducting an
analysis of secondary data extracted from print media reports on Human-Nilgai
negative interactions at the regional scale (tehsils and districts) across
different states of India. Our findings revealed notable variations in conflict
levels among different states, with Bihar emerging as the most affected (86
tehsils and 22 districts), followed by Madhya Pradesh (34 tehsils; 21 districts)
and Uttar Pradesh (33 tehsils; 20 districts). Within Bihar, Muzaffarpur and
East Champaran districts stand out for their high
conflict levels. Crop raiding by different populations of Nilgai is identified
as the primary cause of the negative interaction, with a relative frequency of
occurrence of 98%. Attacks on humans by nilgai, although rare, accounted for a
relative frequency of occurrence of only 1.2%. Additionally, newspapers
reported retaliatory killings, with a relative frequency of occurrence of
0.84%. Between 2018 and 2022, nilgai populations were documented raiding 45
distinct crop types. Analysis of these raids revealed varying frequencies
across different crop categories, with vegetables being the most heavily
targeted (31%), followed by pulses (22%) and cereals (20%). Our study
identifies priority tehsils and districts across different states in the
country where studies aiming at nilgai-crop interactions, population dynamics,
and movement ecology can be carried out to devise effective mitigation
measures.
Keywords: Attacks on humans, Blue Bull,
crop raiding, crop types, farmers, human-wildlife conflict, retaliatory
killings.
Introduction
Human-wildlife negative
interactions (HWNI) refers to the challenges that arise when the existence or
actions of wildlife present a tangible or perceived threat to humans and/or
their interests. This results in disputes among various groups of individuals,
causing adverse effects on both humans and wildlife (IUCN SSC HWCTF 2020). As
the global population expands and urbanization progresses, wildlife habitats
are increasingly endangered by degradation, loss, and fragmentation. The lines
separating human settlements from natural habitats are fading, intensifying
interactions between humans and wildlife. These negative interactions
frequently lead to the loss of crops, livestock & property, and to personal
injuries (Karanth & Kudalkar
2017; Holland et al. 2018). These negative interactions also have indirect
consequences that are challenging to measure, including declines in
psychological well-being and impacts on livelihoods & food security (Barua et al. 2013; Yang et al. 2020). Developing regions of
the world, such as southern and southeastern Asia, are particularly vulnerable
to this issue (Anand & Radhakrishna 2017).
Wild ungulates have been found to
be increasingly involved in raiding crops, damaging properties, attacks on
humans, vehicle collisions, and competition with & transmission of diseases
to livestock, causing human-ungulate negative interactions across the globe
(Chauhan et al. 2009; Kuemmerle et al. 2011; Acevedo
et al. 2014; Duarte et al. 2015; Colino-rabanal et
al. 2018; Gross et al. 2018). The introduction of the Wildlife Protection Act
(1972) and its associated management actions, coupled with incompatible land
use practices, have made human-ungulate negative interactions frequent in India
(Chauhan & Singh 1990; Chauhan et al. 2009; Bajwa
& Chauhan 2019).
Nilgai, also known as Blue Bull Boselaphus tragocamelus
Pallas, 1766, is an interaction-prone ungulate species in India (Sekhar 1998; Chhangani et al. 2008; Kumar et al. 2017; Bajwa & Chauhan 2019). Although widely distributed (Karanth et al. 2009), there is a scarcity of knowledge on
interaction distribution range, and few studies have attempted to address this
issue (Chauhan et al. 2010; Chauhan 2011). The species has been found to be
increasingly involved in road mishaps, human-human conflicts over their
population management practices, and attacks on humans (Dharaiya
2012; Vishnoi 2016; Khan et al. 2019; Gulati et al.
2021; Gorchiya et al. 2022). However, a comprehensive
study of the interactions of different populations with humans across their
range has not been assessed. The species is well-known as a crop pest in India
(Chauhan & Singh 1990; Goyal & Rajpurohit
2000). Despite this, we have a limited understanding of nilgai-crop
interactions, notably it is not known whether some crop types influence
human-nilgai negative interactions more than others. In this review, we
attempted to address these questions through a systematic survey and analysis
of newspaper reports. We first identified different types of human-nilgai
negative interactions and their relative frequency of occurrences in India.
Similarly, we estimated the relative frequencies of different crop types raided
by nilgai in India. The conflict hotspot was identified and mapped at a smaller
administrative level based on the reported location and conflict intensity,
estimated from various news sources.
Study Area
The present study focuses on analysing print media coverage of human-nilgai negative
interactions. The research spans various sub-districts in Indian states where
nilgai populations are prevalent, including Andhra Pradesh, Bihar, Chhattisgarh,
Gujarat, Haryana, Himachal Pradesh, Jharkhand, Madhya Pradesh, Maharashtra,
Odisha, Punjab, Rajasthan, Telangana, Uttarakhand, Uttar Pradesh, and West
Bengal (Johnsingh & Manjrekar 2016; Jhala et al. 2019).
These states can be broadly
classified based on their geological zones and geographical locations. The
classifications include the Shivalik Hills landscape
(Himachal Pradesh, Punjab, Haryana, Uttarakhand, and Uttar Pradesh), Gangetic
Plains landscape (Uttarakhand, Uttar Pradesh, Bihar), western Indian landscape
(Rajasthan and Gujarat), central Indian landscape (Madhya Pradesh, Maharashtra,
Chhattisgarh, Jharkhand, and Odisha), Eastern Ghats landscape (Andhra Pradesh,
Telangana, and Odisha), and northeastern hills (West Bengal).
The area encompasses diverse
biogeographic zones, ranging from the western Himalaya, Punjab, and Gangetic
Plains in the north to the desert and semi-arid areas in the west, Deccan
Peninsula in the south, and eastern highlands in the east (Menon 2014). The
landscapes of these states harbour a unique and rich
assemblage of flora and fauna in their protected areas (Jhala
et al. 2019).
Certain states in the study area harbour abundant nilgai populations, prevalent not only
within designated protected areas but also thriving outside their confines.
Material
and Methods
Data collection and analysis
The secondary data on
human-nilgai negative interactions in India was obtained through a systematic
survey of news articles from 2018–2022, for a total duration of five years
(Alexander & Quinn 2008; Athreya et al. 2015).
Mainly considering English and Hindi language-based newspapers for data
collection, we conducted a literature survey in the news section of the Google
search engine using English and Hindi keywords such as ‘crop’, ‘damage’,
‘loss’, ‘menace’, ‘attack’, ‘farm’, and ‘farmer’ in combination with ‘nilgai’
or ‘blue bull’. Additionally, we included vernacular names of the species that
Hindi newspapers might use, such as ‘Ghodroj’, ‘Ghodparas’, ‘Roz’, ‘Rojda’, and ‘Vanroz’ as identified in previous references (Chauhan et
al. 2010; Menon 2014; The Guardian 2014). The literature survey extended
through the last tab.
In the administrative structure
of an Indian state, a district serves as a fundamental division, encompassing
sub-districts known as tehsils or taluks. A tehsil, in turn, is an
administrative unit within the district, constituting an area of land with a
central city or town acting as its administrative centre.
This region may include additional towns and commonly comprises several
villages (https://darpg.gov.in/).
We extracted details on the
location of negative interactions as reported in the newspaper, including
villages and towns, and subsequently identified and listed the corresponding
tehsils for these interaction-affected areas. This process was undertaken by
examining reports in newspapers, and further verification and identification
were conducted by visiting the official websites of the corresponding districts
in the state. Additionally, we utilized the resources available on
(https://grammanchitra.gov.in/GM3/) to ensure comprehensive and accurate
information on interaction-affected tehsils. In reports where we could obtain
information at the district level only, the district name was searched along
with the combinations of previous keywords.
Special attention was paid to categorising the conflict. We defined human-nilgai negative
interactions here as incidents of crop raids, damage to property, attacks on
humans by nilgai, and retaliation against these actions by people (IUCN SSC
HWCTF 2020). Crop raiding was defined as damage to standing crops by feeding
and trampling (Hill 2017). During this literature survey, we also encountered
news reports of nilgai vehicle collisions in the form of either road accidents
or railway accidents. Given the definition of human-wildlife negative
interactions (IUCN SSC HWCTF 2020), we considered them as accidents and did not
include these reports in our study.
We recorded the crop types
affected by nilgai populations in a binary fashion: ‘1’ indicates a raid and
‘0’ no raids. Crops were categorized into five categories: cereals, pulses,
vegetables, oil yielding crops, and other cash crops. In instances where nilgai
raided multiple crops in different villages within a tehsil, we entered each
case separately with corresponding village or town names. For situations where
the news article did not specify the crop name or category, we assigned them to
an unspecified category.
The relative frequency of each
affected crop type was estimated as a percentage by summing up the total raid
cases for that specific crop type and dividing it by the total cases for all
crop types affected. The result was then multiplied by 100 to obtain the
relative frequency in percentage (Table 1). Similarly, the relative frequency
for each interaction category was also estimated.
For spatial mapping of negative
interactions, district-level information was used. We obtained the Survey of
India website (https://www.surveyofindia.gov.in/) GIS database at the district
level for Indian states. The mapping was conducted at two scales: firstly, at
the district level for the most affected state, and secondly, at the country
level for our study area. A hot spot map at the district level was created for
the most affected state by estimating the crop raiding frequency (CRF) across
its various tehsils (Hoare 1999). Here CRF represents the total number of crop
raiding incidents or events across various tehsils of a district throughout the
entire study period. To prevent the over-reporting of the same incident by
different newspapers, we maintained a minimum interval of 11 days between
reports from the same tehsil.
At the country level, spatial
mapping was conducted by summing the number of interaction-affected tehsils in
the corresponding states.
There are two advantages of using
this approach. First, it provides a snapshot sample of the spatial distribution
of human-nilgai interaction in both inside as well as outside protected area networks
across a relatively large geographic area. Second, in India, states such as
Gujarat, Rajasthan, Haryana, and Punjab have no compensation scheme for crop
raids by ungulates and hence the interaction records (Karanth
et al. 2018; Bajwa & Chauhan 2019). In this way,
data were extracted and analysed from online editions
of 13 publications, including 10 Hindi and three English-language newspapers.
Results
Spatial distribution of
interactions in the country
Different newspapers reported a
total of 597 interaction cases in India, spanning 73 districts, encompassing
183 tehsils across 11 states within the timeframe of 2018–2022. However, the
number of conflict cases in each of these tehsils and states suggests that its
severity is different across them.
As depicted in Figure (1) and
Table (2), Bihar emerged as the most frequently affected state, with 22 of 38
districts affected, constituting approximately 58% of all districts. 86 tehsils
were affected, or approximately 47% of the total (183) affected in the country.
Madhya Pradesh and Uttar Pradesh ranked second and third in the list of
affected states.
Madhya Pradesh state has been
witnessing the impact across 21 out of its 53 districts, affecting a total of
34 tehsils. Similarly, Uttar Pradesh is facing challenges, with 20 out of 75
districts, encompassing 33 affected tehsils within the state. Further details
about the situation in other states are outlined in Table (2).
We assessed the intensity of
conflict by calculating the CRF for various districts in Indian states (Table
2). Given that Bihar has the highest number of affected tehsils, we generated a
hot spot map using the CRF values assigned to its districts (Figure 2). Our
finding revealed that both Muzaffarpur and eastern Champaran
have been experiencing a higher intensity of conflict compared to other
districts in Bihar, with all of their tehsils affected.
Relative frequency of different
conflict categories
Out of 597 conflict cases
reported in India, 98% (585 cases) were attributed to crop raids by nilgai.
Attacks on humans accounted for 1.2% (seven cases), while 0.84% (five cases)
involved the retaliatory killing of nilgai by humans (Figure 3). Notably, our
survey did not uncover any news reports of property damage caused by nilgai
during the specified period.
Relative frequency of different
crops raided by nilgai
We found that different nilgai
populations have damaged 45 crop types in India. To gain a deeper understanding
of this impact, we categorized these crop types into specific crop categories.
Among these crop categories, vegetables had the highest relative frequency of
raid (32%) by nilgai (Figure 4). A total of 18 crop types were damaged in this
category, with relatively frequent damage observed in two crop types:
unspecified vegetables (7.6%) and potato crops (6.2%) (Table 1).
After vegetables, pulses were the
second most frequently raided (22%) category. Although eight crop types were
damaged in this category, Chickpea, Pigeon Pea, and Pea were the three crop
types particularly vulnerable to nilgai raids. Cereals ranked third (20%) in
the most affected crop category, with wheat and maize being crop types
frequently sustained nilgai raids.
Oil yielding crops were least
raided category, accounting for only 7.6%. Further details on other crop
categories and their types affected are given in Table 1.
Attacks on humans by nilgai
We found only seven news reports
of nilgai attacks on humans during our study period, showing the rarity of such
attacks. Five people died, and three were injured in these attacks. The victims
were farmers. Most of them were working on the farm, while one victim was
guarding the crops at night. These news reports did not specify whether the
attack was intentional or in self-defence, leaving
uncertainty about the motivations behind these rare occurrences.
Retaliatory killing of nilgai by
humans
Newspapers reported five
incidents of retaliatory killing of the species by humans. Farmers retaliated
against frequent crop raids by fencing their farms with high-tension electric
wires or by shooting the animal while raiding crops, which led to nilgai
deaths.
Discussion
This was the first attempt to map
and assess the spatial distribution of human-nilgai negative interactions in
India at the district and smaller administrative scales. Our findings revealed
that, as compared to other states in India, Bihar has faced relatively severe
human-nilgai conflict, with most of its districts and tehsils being affected.
This finding was not surprising, because due to the severity of this issue the
state culled 4,729 nilgais during 2016–2019 (Khan 2021). Madhya Pradesh and
Uttar Pradesh ranked second and third, respectively. A total of 3,278 cases of
crop raids by nilgai were reported during 2009–2013 in Madhya Pradesh, and the
state government had to pay 1.2 crore Indian Rupees (US$ 146,568) as
compensation to victims (Babbar et al. 2022).
Previous studies suggest that Uttar Pradesh has the largest population of
nilgai (2,54,449) in India (Chauhan 2011). This state has been facing crop
raiding by nilgai since the 1990s (Qureshi 1991). In 1995 and 1996, considering
the severity of crop damage by nilgai, the government issued a permission
letter and eliminated 270 individuals in the Etah
district of the state (Chauhan et al. 2010).
The non-existence of studies on
the nilgai population trend coupled with land use land cover dynamics in these
three states has hampered our understanding of why these states have been
facing a relatively higher intensity of conflict. Interestingly states such as
Rajasthan, Punjab, and Haryana had fewer cases of conflict, despite previous
studies suggesting otherwise (Chopra & Rai 2009; Meena et al. 2014; Johnson
et al. 2018; Bajwa & Chauhan 2019; Kumar et al.
2022). One possible explanation could be that these states are dominated by the
Bishnoi community, who tend to tolerate nilgai raids
rather than reporting them, due to their cultural and religious sentiments (Sankar & Goyal 2004). Another factor could be
under-reporting of conflict by print media, attributed to the lack of public
interest in incidents of crop and property damage caused by wildlife (Neupane et al. 2013). Even in the case of Bihar state, the
frequency of crop raids by nilgai in different districts is almost negligible
compared to findings in previous studies (Bayani et
al. 2016; Bayani & Watve
2016). In many parts of India, crop raiding by nilgai has become so frequent
that print media rarely cover all incidents unless they become topics of debate
among politicians or provoke mass protests by victims (Vishnoi
2016; Times of India 2024).
Under-reporting by print media
has already been documented in previous studies (Neupane
et al. 2013; Paudel et al. 2022). Our results suggest
that crop raiding by nilgai is a primary cause of negative interaction with
humans across its distribution range, which corresponds to previous findings
(Sekhar 1998; Chhangani et al 2008; Kumar et al.
2017; Bajwa & Chauhan 2019). Given India’s status
as an agrarian country, this issue presents a significant threat to the
livelihoods of farmers and to the food security of subsistence farmers in these
affected states (Barua et al. 2013; Rathi et al. 2020). It was found that in the study area,
nilgai raided 45 types of crops in a time frame of five years. Nilgai, being a
mixed feeder (Hines 2016) weighing over 250 kg (Sheffield et al. 1983), is capable
of causing extensive damage to standing crops and orchards by selective feeding
and trampling. However, vegetables, pulses, and cereals were raided the most
among different crop categories. These results correspond to the previous
findings where nilgai demonstrated preferences towards vegetable, pulse, and
cereal crops (Aryal 2007; Kumar et al. 2017, 2022; Khanal et al. 2018). One possible explanation could be that
due to their higher nutritional value and palatability, these crops may have
preferentially foraged over others (Biru & Bekele
2012).
During our literature survey, we
found that many newspapers reported escalating nilgai raids, leading farmers in
severely affected areas to increasingly avoid cultivating vegetables and
pulses, highlighting a pressing need for effective management. Our results
indicate that attacks on humans by nilgai are rare, probably due to their timid
nature. Farmers are particularly vulnerable to such attacks while guarding
their crops or driving away the animal due to its sheer size and agility. Our
study revealed instances of retaliatory killing of nilgai through methods such
as electrocution and shooting. This finding corresponds to a previous study
where villagers poisoned nilgais to protect their crops (Qureshi 1991).
Although this approach to
studying human-nilgai conflict is advantageous in quickly covering a relatively
large area and obtaining data from areas without records, it has limitations.
Due to the incomplete media coverage, we could not obtain any data on some
other crucial aspects of this conflict, such as population estimates of nilgai
in affected areas, their phenological preferences for different crop types, sex
associated with crop raiding, temporal patterns of crop raids, extent of damage
inflicted on different crop types and the motivations behind their attacks on
humans. Our findings have revealed that there is an urgent need to conduct
studies on the population dynamics of nilgai in different affected tehsils of
Bihar, Madhya Pradesh, and Uttar Pradesh states. Based on the intensity of
negative interactions (CRF), focal districts and its corresponding tehsils can
be chosen from the list we have provided in this article (see supplementary
Table 1 for details). Studies addressing nilgai-crop interactions would be
critical in identifying high-risk crops and formulating appropriate mitigation
measures. Lastly, Studies on their habitat and movement ecology in these
affected tehsils using radio telemetry will enable pinpointing high-risk zones,
understanding habitat preferences, and developing targeted strategies for
mitigation, promoting coexistence through proactive management based on
real-time insights.
Table 1.
Various crops raided by nilgai with their relative frequencies of raid.
|
Crop categories |
Crop types affected |
Relative frequencies of raid
(%) |
|
Vegetables |
Unspecified vegetables |
7.55 |
|
Potato |
6.21 |
|
|
Cauliflower |
2.72 |
|
|
Tomato |
2.11 |
|
|
Brinjal |
1.85 |
|
|
Coriander |
1.85 |
|
|
Onion |
1.80 |
|
|
Cabbage |
1.39 |
|
|
Ash gourd |
1.13 |
|
|
Bitter gourd |
1.13 |
|
|
Fenugreek |
0.92 |
|
|
Garlic |
0.62 |
|
|
Sweet potato |
0.56 |
|
|
Chilli |
0.56 |
|
|
Pointed gourd |
0.41 |
|
|
Turmeric |
0.26 |
|
|
Okra |
0.05 |
|
|
Ridge gourd |
0.05 |
|
|
Pulses |
Chickpea |
7.40 |
|
Pigeon pea |
4.42 |
|
|
Pea |
3.80 |
|
|
Red lentil |
2.72 |
|
|
Unspecified pulses |
1.90 |
|
|
Green gram |
0.72 |
|
|
Lobia pulse |
0.46 |
|
|
Grass pea |
0.41 |
|
|
Cereals |
Wheat |
10.73 |
|
Maize |
5.34 |
|
|
Paddy |
3.24 |
|
|
Sorghum |
0.31 |
|
|
Pearl millet |
0.31 |
|
|
Unspecified |
Unspecified Crops |
10.84 |
|
Other cash crops |
Banana |
1.64 |
|
Mango plants |
1.54 |
|
|
Sugarcane |
1.39 |
|
|
Opium |
1.23 |
|
|
Cotton |
1.23 |
|
|
Guava |
1.18 |
|
|
Papaya |
0.36 |
|
|
Lychee plants |
0.05 |
|
|
Oil yielding crops |
Mustard |
4.88 |
|
Soyabean |
1.23 |
|
|
Unspecified oilseeds |
0.67 |
|
|
Linseed |
0.62 |
|
|
Ground nut |
0.21 |
|
|
|
|
SUM = 100% |
Table 2.
List of affected districts across Indian states, as reported by newspapers,
along with its corresponding crop raiding frequency estimates (CRF).
|
State |
District |
CRF |
|
Bihar |
Arwal |
1 |
|
Bihar |
Aurangabad |
2 |
|
Bihar |
Begusarai |
1 |
|
Bihar |
Bhagalpur |
1 |
|
Bihar |
Bhojpur |
1 |
|
Bihar |
Buxar |
5 |
|
Bihar |
Darbhanga |
3 |
|
Bihar |
East Champaran |
29 |
|
Bihar |
Gopalganj |
2 |
|
Bihar |
Kaimur |
1 |
|
Bihar |
Lakhisarai |
1 |
|
Bihar |
Muzaffarpur |
37 |
|
Bihar |
Samastipur |
1 |
|
Bihar |
Saran |
1 |
|
Bihar |
Sheikhpura |
8 |
|
Bihar |
Sheohar |
7 |
|
Bihar |
Sitamarhi |
3 |
|
Bihar |
Siwan |
1 |
|
Bihar |
Supaul |
1 |
|
Bihar |
Vaishali |
2 |
|
Bihar |
West Champaran |
7 |
|
Madhya Pradesh |
Dhar |
5 |
|
Madhya Pradesh |
Gwalior |
3 |
|
Madhya Pradesh |
Sheopur |
7 |
|
Madhya Pradesh |
Raisen |
1 |
|
Madhya Pradesh |
Ratlam |
7 |
|
Madhya Pradesh |
Ujjain |
2 |
|
Madhya Pradesh |
Sagar |
1 |
|
Madhya Pradesh |
Damoh |
1 |
|
Madhya Pradesh |
Indore |
1 |
|
Madhya Pradesh |
Neemuch |
3 |
|
Madhya Pradesh |
Dewas |
1 |
|
Madhya Pradesh |
Shivpuri |
1 |
|
Madhya Pradesh |
Mandsaur |
3 |
|
Madhya Pradesh |
Tikamgarh |
1 |
|
Madhya Pradesh |
Jhabua |
2 |
|
Madhya Pradesh |
Shajapur |
1 |
|
Madhya Pradesh |
Rewa |
1 |
|
Uttar Pradesh |
Deoria |
1 |
|
Uttar Pradesh |
Azamgarh |
8 |
|
Uttar Pradesh |
Saharanpur |
3 |
|
Uttar Pradesh |
Gorakhpur |
2 |
|
Uttar Pradesh |
Basti |
1 |
|
Uttar Pradesh |
Kannauj |
1 |
|
Uttar Pradesh |
Bhadohi |
2 |
|
Uttar Pradesh |
Prayagraj |
4 |
|
Uttar Pradesh |
Moradabad |
3 |
|
Uttar Pradesh |
Aligarh |
1 |
|
Uttar Pradesh |
Lalitpur |
2 |
|
Uttar Pradesh |
Meerut |
1 |
|
Uttar Pradesh |
Maharajganj |
2 |
|
Uttar Pradesh |
Unnao |
1 |
|
Uttar Pradesh |
Hathras |
1 |
|
Uttar Pradesh |
Ghazipur |
1 |
|
Rajasthan |
Bhilwara |
4 |
|
Rajasthan |
Chittorgarh |
2 |
|
Rajasthan |
Jalore |
1 |
|
Rajasthan |
Pratapgarh |
2 |
|
Rajasthan |
Nagaur |
1 |
|
Rajasthan |
Jhalawar |
1 |
|
Jharkhand |
Palamu |
2 |
|
Jharkhand |
Garhwa |
3 |
|
Jharkhand |
Koderma |
1 |
|
Maharashtra |
Wardha |
1 |
|
Maharashtra |
Akola |
1 |
|
Maharashtra |
Chandrapur |
1 |
|
Haryana |
Palwal |
2 |
|
Haryana |
Fatehabad |
1 |
|
Punjab |
Pathankot |
1 |
|
Punjab |
Rupnagar/Ropar |
1 |
|
Odisha |
Sundargarh |
1 |
|
Gujarat |
Surendranagar |
1 |
For
figures & Supplementary Table - - click here for full PDF
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